FlowAnchor：穩定影片編輯信號的無反轉編輯技術

摘要

我們提出FlowAnchor，這是一種用於穩定高效、免反轉的基於流模型的影片編輯的免訓練框架。近期免反轉編輯方法透過直接以編輯信號引導採樣軌跡，在圖像領域展現出卓越的效率和結構保持能力。然而將此範式擴展至影片仍具挑戰性，在多物體場景或幀數增加時常出現失效。我們發現根本原因在於高維影片潛在空間中編輯信號的不穩定性，這種不穩定性源自空間定位不精確和長度導致的幅度衰減。為解決此問題，FlowAnchor明確錨定了編輯位置與編輯強度：引入空間感知注意力優化機制，強制文本引導與空間區域的一致性對齊；並採用自適應幅度調製技術，動態保持充足的編輯強度。這些機制共同穩定編輯信號，引導基於流模型的演化朝向目標分佈。大量實驗表明，FlowAnchor在挑戰性的多物體與快速運動場景中，能實現更精準、時間連貫且計算高效的影片編輯。項目頁面請見：https://cuc-mipg.github.io/FlowAnchor.github.io/。

English

We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive efficiency and structure preservation in images by directly steering the sampling trajectory with an editing signal. However, extending this paradigm to videos remains challenging, often failing in multi-object scenes or with increased frame counts. We identify the root cause as the instability of the editing signal in high-dimensional video latent spaces, which arises from imprecise spatial localization and length-induced magnitude attenuation. To overcome this challenge, FlowAnchor explicitly anchors both where to edit and how strongly to edit. It introduces Spatial-aware Attention Refinement, which enforces consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation, which adaptively preserves sufficient editing strength. Together, these mechanisms stabilize the editing signal and guide the flow-based evolution toward the desired target distribution. Extensive experiments demonstrate that FlowAnchor achieves more faithful, temporally coherent, and computationally efficient video editing across challenging multi-object and fast-motion scenarios. The project page is available at https://cuc-mipg.github.io/FlowAnchor.github.io/.

FlowAnchor：穩定影片編輯信號的無反轉編輯技術

FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

摘要

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